Overview

Dataset statistics

Number of variables31
Number of observations2240
Missing cells0
Missing cells (%)0.0%
Duplicate rows179
Duplicate rows (%)8.0%
Total size in memory542.6 KiB
Average record size in memory248.1 B

Variable types

Numeric17
Categorical13
DateTime1

Alerts

Dataset has 179 (8.0%) duplicate rowsDuplicates
AcceptedCmp3 is highly imbalanced (62.4%)Imbalance
AcceptedCmp4 is highly imbalanced (61.7%)Imbalance
AcceptedCmp5 is highly imbalanced (62.4%)Imbalance
AcceptedCmp1 is highly imbalanced (65.6%)Imbalance
AcceptedCmp2 is highly imbalanced (89.7%)Imbalance
Complain is highly imbalanced (92.3%)Imbalance
TotalAcceptedCmp is highly imbalanced (57.2%)Imbalance
Recency has 28 (1.2%) zerosZeros
MntFruits has 400 (17.9%) zerosZeros
MntFishProducts has 384 (17.1%) zerosZeros
MntSweetProducts has 419 (18.7%) zerosZeros
MntGoldProds has 61 (2.7%) zerosZeros
NumDealsPurchases has 46 (2.1%) zerosZeros
NumWebPurchases has 49 (2.2%) zerosZeros
NumCatalogPurchases has 586 (26.2%) zerosZeros

Reproduction

Analysis started2024-02-16 15:47:48.714688
Analysis finished2024-02-16 15:48:30.392135
Duration41.68 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Year_Birth
Real number (ℝ)

Distinct59
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.8058
Minimum1893
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:30.567387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1893
5-th percentile1950
Q11959
median1970
Q31977
95-th percentile1988
Maximum1996
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.984069
Coefficient of variation (CV)0.0060869739
Kurtosis0.71746444
Mean1968.8058
Median Absolute Deviation (MAD)9
Skewness-0.34994386
Sum4410125
Variance143.61792
MonotonicityNot monotonic
2024-02-16T10:48:30.750627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1976 89
 
4.0%
1971 87
 
3.9%
1975 83
 
3.7%
1972 79
 
3.5%
1978 77
 
3.4%
1970 77
 
3.4%
1973 74
 
3.3%
1965 74
 
3.3%
1969 71
 
3.2%
1974 69
 
3.1%
Other values (49) 1460
65.2%
ValueCountFrequency (%)
1893 1
 
< 0.1%
1899 1
 
< 0.1%
1900 1
 
< 0.1%
1940 1
 
< 0.1%
1941 1
 
< 0.1%
1943 7
0.3%
1944 7
0.3%
1945 8
0.4%
1946 16
0.7%
1947 16
0.7%
ValueCountFrequency (%)
1996 2
 
0.1%
1995 5
 
0.2%
1994 3
 
0.1%
1993 5
 
0.2%
1992 13
0.6%
1991 15
0.7%
1990 18
0.8%
1989 30
1.3%
1988 29
1.3%
1987 27
1.2%

Education
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Undergraduate
1127 
Postgraduate
856 
2n Cycle
203 
Below Undergraduate
 
54

Length

Max length19
Median length13
Mean length12.309375
Min length8

Characters and Unicode

Total characters27573
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUndergraduate
2nd rowUndergraduate
3rd rowUndergraduate
4th rowUndergraduate
5th rowPostgraduate

Common Values

ValueCountFrequency (%)
Undergraduate 1127
50.3%
Postgraduate 856
38.2%
2n Cycle 203
 
9.1%
Below Undergraduate 54
 
2.4%

Length

2024-02-16T10:48:30.994921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:31.125050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
undergraduate 1181
47.3%
postgraduate 856
34.3%
2n 203
 
8.1%
cycle 203
 
8.1%
below 54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 4074
14.8%
e 3475
12.6%
d 3218
11.7%
r 3218
11.7%
t 2893
10.5%
g 2037
7.4%
u 2037
7.4%
n 1384
 
5.0%
U 1181
 
4.3%
o 910
 
3.3%
Other values (10) 3146
11.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24819
90.0%
Uppercase Letter 2294
 
8.3%
Space Separator 257
 
0.9%
Decimal Number 203
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4074
16.4%
e 3475
14.0%
d 3218
13.0%
r 3218
13.0%
t 2893
11.7%
g 2037
8.2%
u 2037
8.2%
n 1384
 
5.6%
o 910
 
3.7%
s 856
 
3.4%
Other values (4) 717
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
U 1181
51.5%
P 856
37.3%
C 203
 
8.8%
B 54
 
2.4%
Space Separator
ValueCountFrequency (%)
257
100.0%
Decimal Number
ValueCountFrequency (%)
2 203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27113
98.3%
Common 460
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4074
15.0%
e 3475
12.8%
d 3218
11.9%
r 3218
11.9%
t 2893
10.7%
g 2037
7.5%
u 2037
7.5%
n 1384
 
5.1%
U 1181
 
4.4%
o 910
 
3.4%
Other values (8) 2686
9.9%
Common
ValueCountFrequency (%)
257
55.9%
2 203
44.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4074
14.8%
e 3475
12.6%
d 3218
11.7%
r 3218
11.7%
t 2893
10.5%
g 2037
7.4%
u 2037
7.4%
n 1384
 
5.0%
U 1181
 
4.3%
o 910
 
3.3%
Other values (10) 3146
11.4%

Marital_Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
In a Relationship
1444 
Single
796 

Length

Max length17
Median length17
Mean length13.091071
Min length6

Characters and Unicode

Total characters29324
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowIn a Relationship
4th rowIn a Relationship
5th rowIn a Relationship

Common Values

ValueCountFrequency (%)
In a Relationship 1444
64.5%
Single 796
35.5%

Length

2024-02-16T10:48:31.272243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:31.395736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
in 1444
28.2%
a 1444
28.2%
relationship 1444
28.2%
single 796
15.5%

Most occurring characters

ValueCountFrequency (%)
n 3684
12.6%
i 3684
12.6%
2888
9.8%
a 2888
9.8%
e 2240
 
7.6%
l 2240
 
7.6%
I 1444
 
4.9%
R 1444
 
4.9%
t 1444
 
4.9%
o 1444
 
4.9%
Other values (5) 5924
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22752
77.6%
Uppercase Letter 3684
 
12.6%
Space Separator 2888
 
9.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3684
16.2%
i 3684
16.2%
a 2888
12.7%
e 2240
9.8%
l 2240
9.8%
t 1444
 
6.3%
o 1444
 
6.3%
s 1444
 
6.3%
h 1444
 
6.3%
p 1444
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
I 1444
39.2%
R 1444
39.2%
S 796
21.6%
Space Separator
ValueCountFrequency (%)
2888
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26436
90.2%
Common 2888
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3684
13.9%
i 3684
13.9%
a 2888
10.9%
e 2240
8.5%
l 2240
8.5%
I 1444
 
5.5%
R 1444
 
5.5%
t 1444
 
5.5%
o 1444
 
5.5%
s 1444
 
5.5%
Other values (4) 4480
16.9%
Common
ValueCountFrequency (%)
2888
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3684
12.6%
i 3684
12.6%
2888
9.8%
a 2888
9.8%
e 2240
 
7.6%
l 2240
 
7.6%
I 1444
 
4.9%
R 1444
 
4.9%
t 1444
 
4.9%
o 1444
 
4.9%
Other values (5) 5924
20.2%

Income
Real number (ℝ)

Distinct1998
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52207.087
Minimum1730
Maximum666666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:31.551081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile19101.05
Q135434.75
median51369
Q368289.75
95-th percentile83927
Maximum666666
Range664936
Interquartile range (IQR)32855

Descriptive statistics

Standard deviation25073.376
Coefficient of variation (CV)0.48026767
Kurtosis160.50823
Mean52207.087
Median Absolute Deviation (MAD)16422.5
Skewness6.7744652
Sum1.1694387 × 108
Variance6.2867418 × 108
MonotonicityNot monotonic
2024-02-16T10:48:31.764940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500 12
 
0.5%
35860 4
 
0.2%
80134 3
 
0.1%
18690 3
 
0.1%
46098 3
 
0.1%
63841 3
 
0.1%
18929 3
 
0.1%
83844 3
 
0.1%
48432 3
 
0.1%
39922 3
 
0.1%
Other values (1988) 2200
98.2%
ValueCountFrequency (%)
1730 1
< 0.1%
2447 1
< 0.1%
3502 1
< 0.1%
4023 1
< 0.1%
4428 1
< 0.1%
4861 1
< 0.1%
5305 1
< 0.1%
5648 1
< 0.1%
6560 1
< 0.1%
6835 1
< 0.1%
ValueCountFrequency (%)
666666 1
< 0.1%
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
105471 1
< 0.1%

Kidhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1293 
1
899 
2
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Length

2024-02-16T10:48:31.981400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:32.141718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1293
57.7%
1 899
40.1%
2 48
 
2.1%

Teenhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1158 
1
1030 
2
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Length

2024-02-16T10:48:32.335665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:32.496324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1158
51.7%
1 1030
46.0%
2 52
 
2.3%
Distinct663
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Minimum2012-07-30 00:00:00
Maximum2014-06-29 00:00:00
2024-02-16T10:48:32.727078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:33.037544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Recency
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.109375
Minimum0
Maximum99
Zeros28
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:33.309311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.962453
Coefficient of variation (CV)0.58975405
Kurtosis-1.2018968
Mean49.109375
Median Absolute Deviation (MAD)25
Skewness-0.0019866586
Sum110005
Variance838.82367
MonotonicityNot monotonic
2024-02-16T10:48:33.584678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 37
 
1.7%
30 32
 
1.4%
54 32
 
1.4%
46 31
 
1.4%
92 30
 
1.3%
49 30
 
1.3%
65 30
 
1.3%
3 29
 
1.3%
29 29
 
1.3%
71 29
 
1.3%
Other values (90) 1931
86.2%
ValueCountFrequency (%)
0 28
1.2%
1 24
1.1%
2 28
1.2%
3 29
1.3%
4 27
1.2%
5 15
0.7%
6 21
0.9%
7 12
0.5%
8 25
1.1%
9 24
1.1%
ValueCountFrequency (%)
99 17
0.8%
98 22
1.0%
97 20
0.9%
96 25
1.1%
95 19
0.8%
94 26
1.2%
93 21
0.9%
92 30
1.3%
91 18
0.8%
90 20
0.9%

MntWines
Real number (ℝ)

Distinct776
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.93571
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:33.790265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123.75
median173.5
Q3504.25
95-th percentile1000
Maximum1493
Range1493
Interquartile range (IQR)480.5

Descriptive statistics

Standard deviation336.59739
Coefficient of variation (CV)1.1074625
Kurtosis0.59874359
Mean303.93571
Median Absolute Deviation (MAD)164.5
Skewness1.1757706
Sum680816
Variance113297.8
MonotonicityNot monotonic
2024-02-16T10:48:34.010286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 42
 
1.9%
5 40
 
1.8%
1 37
 
1.7%
6 37
 
1.7%
4 33
 
1.5%
8 30
 
1.3%
3 30
 
1.3%
9 28
 
1.2%
12 25
 
1.1%
10 24
 
1.1%
Other values (766) 1914
85.4%
ValueCountFrequency (%)
0 13
 
0.6%
1 37
1.7%
2 42
1.9%
3 30
1.3%
4 33
1.5%
5 40
1.8%
6 37
1.7%
7 22
1.0%
8 30
1.3%
9 28
1.2%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 2
0.1%
1486 1
< 0.1%
1478 2
0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

MntFruits
Real number (ℝ)

ZEROS 

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.302232
Minimum0
Maximum199
Zeros400
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:34.229952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile123
Maximum199
Range199
Interquartile range (IQR)32

Descriptive statistics

Standard deviation39.773434
Coefficient of variation (CV)1.5121695
Kurtosis4.0509763
Mean26.302232
Median Absolute Deviation (MAD)8
Skewness2.1020633
Sum58917
Variance1581.926
MonotonicityNot monotonic
2024-02-16T10:48:34.497293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 400
 
17.9%
1 162
 
7.2%
2 120
 
5.4%
3 116
 
5.2%
4 104
 
4.6%
7 67
 
3.0%
5 65
 
2.9%
6 62
 
2.8%
12 50
 
2.2%
8 48
 
2.1%
Other values (148) 1046
46.7%
ValueCountFrequency (%)
0 400
17.9%
1 162
7.2%
2 120
 
5.4%
3 116
 
5.2%
4 104
 
4.6%
5 65
 
2.9%
6 62
 
2.8%
7 67
 
3.0%
8 48
 
2.1%
9 35
 
1.6%
ValueCountFrequency (%)
199 2
0.1%
197 1
 
< 0.1%
194 3
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
181 1
 
< 0.1%

MntMeatProducts
Real number (ℝ)

Distinct558
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.95
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:34.801869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median67
Q3232
95-th percentile687.1
Maximum1725
Range1725
Interquartile range (IQR)216

Descriptive statistics

Standard deviation225.71537
Coefficient of variation (CV)1.3519938
Kurtosis5.5167241
Mean166.95
Median Absolute Deviation (MAD)59
Skewness2.0832331
Sum373968
Variance50947.429
MonotonicityNot monotonic
2024-02-16T10:48:34.964077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 53
 
2.4%
5 50
 
2.2%
11 49
 
2.2%
8 46
 
2.1%
6 43
 
1.9%
10 40
 
1.8%
3 40
 
1.8%
9 38
 
1.7%
16 36
 
1.6%
12 35
 
1.6%
Other values (548) 1810
80.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
 
0.6%
2 30
1.3%
3 40
1.8%
4 30
1.3%
5 50
2.2%
6 43
1.9%
7 53
2.4%
8 46
2.1%
9 38
1.7%
ValueCountFrequency (%)
1725 2
0.1%
1622 1
< 0.1%
1607 1
< 0.1%
1582 1
< 0.1%
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%

MntFishProducts
Real number (ℝ)

ZEROS 

Distinct182
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.525446
Minimum0
Maximum259
Zeros384
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:35.118729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile168.05
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.628979
Coefficient of variation (CV)1.4557849
Kurtosis3.0964609
Mean37.525446
Median Absolute Deviation (MAD)12
Skewness1.919769
Sum84057
Variance2984.3254
MonotonicityNot monotonic
2024-02-16T10:48:35.275808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 384
 
17.1%
2 156
 
7.0%
3 130
 
5.8%
4 108
 
4.8%
6 82
 
3.7%
7 66
 
2.9%
8 58
 
2.6%
10 55
 
2.5%
13 48
 
2.1%
12 47
 
2.1%
Other values (172) 1106
49.4%
ValueCountFrequency (%)
0 384
17.1%
1 10
 
0.4%
2 156
7.0%
3 130
 
5.8%
4 108
 
4.8%
5 1
 
< 0.1%
6 82
 
3.7%
7 66
 
2.9%
8 58
 
2.6%
10 55
 
2.5%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 3
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 2
0.1%
237 2
0.1%

MntSweetProducts
Real number (ℝ)

ZEROS 

Distinct177
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.062946
Minimum0
Maximum263
Zeros419
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:35.452042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile126
Maximum263
Range263
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.280498
Coefficient of variation (CV)1.5253512
Kurtosis4.3765483
Mean27.062946
Median Absolute Deviation (MAD)8
Skewness2.1360807
Sum60621
Variance1704.0796
MonotonicityNot monotonic
2024-02-16T10:48:35.618359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 419
 
18.7%
1 161
 
7.2%
2 128
 
5.7%
3 101
 
4.5%
4 82
 
3.7%
5 65
 
2.9%
6 64
 
2.9%
7 57
 
2.5%
8 56
 
2.5%
12 45
 
2.0%
Other values (167) 1062
47.4%
ValueCountFrequency (%)
0 419
18.7%
1 161
 
7.2%
2 128
 
5.7%
3 101
 
4.5%
4 82
 
3.7%
5 65
 
2.9%
6 64
 
2.9%
7 57
 
2.5%
8 56
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
263 1
 
< 0.1%
262 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 3
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%

MntGoldProds
Real number (ℝ)

ZEROS 

Distinct213
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.021875
Minimum0
Maximum362
Zeros61
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:35.761709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24
Q356
95-th percentile165.05
Maximum362
Range362
Interquartile range (IQR)47

Descriptive statistics

Standard deviation52.167439
Coefficient of variation (CV)1.1850345
Kurtosis3.5517093
Mean44.021875
Median Absolute Deviation (MAD)18
Skewness1.8861056
Sum98609
Variance2721.4417
MonotonicityNot monotonic
2024-02-16T10:48:35.906498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 73
 
3.3%
4 70
 
3.1%
3 69
 
3.1%
5 63
 
2.8%
12 63
 
2.8%
2 62
 
2.8%
0 61
 
2.7%
6 57
 
2.5%
7 54
 
2.4%
10 49
 
2.2%
Other values (203) 1619
72.3%
ValueCountFrequency (%)
0 61
2.7%
1 73
3.3%
2 62
2.8%
3 69
3.1%
4 70
3.1%
5 63
2.8%
6 57
2.5%
7 54
2.4%
8 40
1.8%
9 44
2.0%
ValueCountFrequency (%)
362 1
< 0.1%
321 1
< 0.1%
291 1
< 0.1%
262 1
< 0.1%
249 1
< 0.1%
248 1
< 0.1%
247 1
< 0.1%
246 1
< 0.1%
245 1
< 0.1%
242 2
0.1%

NumDealsPurchases
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.325
Minimum0
Maximum15
Zeros46
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:36.046295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9322375
Coefficient of variation (CV)0.83106989
Kurtosis8.9369143
Mean2.325
Median Absolute Deviation (MAD)1
Skewness2.4185694
Sum5208
Variance3.7335418
MonotonicityNot monotonic
2024-02-16T10:48:36.189945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 970
43.3%
2 497
22.2%
3 297
 
13.3%
4 189
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
0 46
 
2.1%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
Other values (5) 24
 
1.1%
ValueCountFrequency (%)
0 46
 
2.1%
1 970
43.3%
2 497
22.2%
3 297
 
13.3%
4 189
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
ValueCountFrequency (%)
15 7
 
0.3%
13 3
 
0.1%
12 4
 
0.2%
11 5
 
0.2%
10 5
 
0.2%
9 8
 
0.4%
8 14
 
0.6%
7 40
1.8%
6 61
2.7%
5 94
4.2%

NumWebPurchases
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0848214
Minimum0
Maximum27
Zeros49
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:36.427335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7787141
Coefficient of variation (CV)0.68025352
Kurtosis5.7031284
Mean4.0848214
Median Absolute Deviation (MAD)2
Skewness1.3827943
Sum9150
Variance7.7212523
MonotonicityNot monotonic
2024-02-16T10:48:36.644339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 373
16.7%
1 354
15.8%
3 336
15.0%
4 280
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.3%
0 49
 
2.2%
Other values (5) 91
 
4.1%
ValueCountFrequency (%)
0 49
 
2.2%
1 354
15.8%
2 373
16.7%
3 336
15.0%
4 280
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.3%
ValueCountFrequency (%)
27 2
 
0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
11 44
 
2.0%
10 43
 
1.9%
9 75
 
3.3%
8 102
4.6%
7 155
6.9%
6 205
9.2%
5 220
9.8%

NumCatalogPurchases
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6620536
Minimum0
Maximum28
Zeros586
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:36.826468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9231007
Coefficient of variation (CV)1.0980623
Kurtosis8.0474368
Mean2.6620536
Median Absolute Deviation (MAD)2
Skewness1.8809888
Sum5963
Variance8.5445174
MonotonicityNot monotonic
2024-02-16T10:48:36.941708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 586
26.2%
1 497
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.2%
6 128
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
10 48
 
2.1%
Other values (4) 65
 
2.9%
ValueCountFrequency (%)
0 586
26.2%
1 497
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.2%
6 128
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
28 3
 
0.1%
22 1
 
< 0.1%
11 19
 
0.8%
10 48
 
2.1%
9 42
 
1.9%
8 55
 
2.5%
7 79
3.5%
6 128
5.7%
5 140
6.2%
4 182
8.1%

NumStorePurchases
Real number (ℝ)

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7901786
Minimum0
Maximum13
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:37.466579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2509581
Coefficient of variation (CV)0.56146077
Kurtosis-0.62204828
Mean5.7901786
Median Absolute Deviation (MAD)2
Skewness0.70223729
Sum12970
Variance10.568729
MonotonicityNot monotonic
2024-02-16T10:48:37.603242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 490
21.9%
4 323
14.4%
2 223
10.0%
5 212
9.5%
6 178
 
7.9%
8 149
 
6.7%
7 143
 
6.4%
10 125
 
5.6%
9 106
 
4.7%
12 105
 
4.7%
Other values (4) 186
 
8.3%
ValueCountFrequency (%)
0 15
 
0.7%
1 7
 
0.3%
2 223
10.0%
3 490
21.9%
4 323
14.4%
5 212
9.5%
6 178
 
7.9%
7 143
 
6.4%
8 149
 
6.7%
9 106
 
4.7%
ValueCountFrequency (%)
13 83
 
3.7%
12 105
 
4.7%
11 81
 
3.6%
10 125
 
5.6%
9 106
 
4.7%
8 149
6.7%
7 143
6.4%
6 178
7.9%
5 212
9.5%
4 323
14.4%

NumWebVisitsMonth
Real number (ℝ)

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3165179
Minimum0
Maximum20
Zeros11
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:37.731948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.426645
Coefficient of variation (CV)0.45643503
Kurtosis1.8216138
Mean5.3165179
Median Absolute Deviation (MAD)2
Skewness0.20792556
Sum11909
Variance5.888606
MonotonicityNot monotonic
2024-02-16T10:48:37.861665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 393
17.5%
8 342
15.3%
6 340
15.2%
5 281
12.5%
4 218
9.7%
3 205
9.2%
2 202
9.0%
1 153
 
6.8%
9 83
 
3.7%
0 11
 
0.5%
Other values (6) 12
 
0.5%
ValueCountFrequency (%)
0 11
 
0.5%
1 153
 
6.8%
2 202
9.0%
3 205
9.2%
4 218
9.7%
5 281
12.5%
6 340
15.2%
7 393
17.5%
8 342
15.3%
9 83
 
3.7%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 83
 
3.7%
8 342
15.3%
7 393
17.5%
6 340
15.2%

AcceptedCmp3
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Length

2024-02-16T10:48:38.126435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:38.296588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

AcceptedCmp4
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2073 
1
 
167

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Length

2024-02-16T10:48:38.444642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:38.561045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2073
92.5%
1 167
 
7.5%

AcceptedCmp5
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Length

2024-02-16T10:48:38.695716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:38.898225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2077
92.7%
1 163
 
7.3%

AcceptedCmp1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2096 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Length

2024-02-16T10:48:39.019919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:39.136410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2096
93.6%
1 144
 
6.4%

AcceptedCmp2
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2210 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Length

2024-02-16T10:48:39.249862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:39.355255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2210
98.7%
1 30
 
1.3%

Complain
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2219 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Length

2024-02-16T10:48:39.479315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:39.594798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2219
99.1%
1 21
 
0.9%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1906 
1
334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Length

2024-02-16T10:48:39.715711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:39.833732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1906
85.1%
1 334
 
14.9%

Kids
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
1
1128 
0
638 
2
421 
3
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Length

2024-02-16T10:48:39.953565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:40.070272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1128
50.4%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Expenses
Real number (ℝ)

Distinct1054
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605.79821
Minimum5
Maximum2525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:40.210781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q168.75
median396
Q31045.5
95-th percentile1772.3
Maximum2525
Range2520
Interquartile range (IQR)976.75

Descriptive statistics

Standard deviation602.24929
Coefficient of variation (CV)0.99414174
Kurtosis-0.34193682
Mean605.79821
Median Absolute Deviation (MAD)353
Skewness0.86084051
Sum1356988
Variance362704.2
MonotonicityNot monotonic
2024-02-16T10:48:40.362221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 19
 
0.8%
22 18
 
0.8%
57 16
 
0.7%
44 15
 
0.7%
55 15
 
0.7%
48 14
 
0.6%
20 14
 
0.6%
43 14
 
0.6%
37 14
 
0.6%
38 14
 
0.6%
Other values (1044) 2087
93.2%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 2
 
0.1%
8 4
 
0.2%
9 2
 
0.1%
10 5
0.2%
11 5
0.2%
12 2
 
0.1%
13 6
0.3%
14 3
 
0.1%
15 10
0.4%
ValueCountFrequency (%)
2525 2
0.1%
2524 1
< 0.1%
2486 1
< 0.1%
2440 1
< 0.1%
2352 1
< 0.1%
2349 1
< 0.1%
2346 1
< 0.1%
2302 2
0.1%
2283 1
< 0.1%
2279 1
< 0.1%

TotalAcceptedCmp
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1777 
1
325 
2
 
83
3
 
44
4
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2240
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1777
79.3%
1 325
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Length

2024-02-16T10:48:40.495656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T10:48:40.618310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1777
79.3%
1 325
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1777
79.3%
1 325
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1777
79.3%
1 325
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1777
79.3%
1 325
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1777
79.3%
1 325
 
14.5%
2 83
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

NumTotalPurchases
Real number (ℝ)

Distinct39
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.862054
Minimum0
Maximum44
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:40.745465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median15
Q321
95-th percentile27
Maximum44
Range44
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.6771726
Coefficient of variation (CV)0.51656203
Kurtosis-0.89312271
Mean14.862054
Median Absolute Deviation (MAD)7
Skewness0.25211097
Sum33291
Variance58.938979
MonotonicityNot monotonic
2024-02-16T10:48:40.878369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
7 149
 
6.7%
5 145
 
6.5%
4 128
 
5.7%
6 123
 
5.5%
17 116
 
5.2%
9 102
 
4.6%
19 101
 
4.5%
16 101
 
4.5%
21 95
 
4.2%
8 94
 
4.2%
Other values (29) 1086
48.5%
ValueCountFrequency (%)
0 4
 
0.2%
1 4
 
0.2%
2 3
 
0.1%
4 128
5.7%
5 145
6.5%
6 123
5.5%
7 149
6.7%
8 94
4.2%
9 102
4.6%
10 80
3.6%
ValueCountFrequency (%)
44 1
 
< 0.1%
43 1
 
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
35 1
 
< 0.1%
34 4
 
0.2%
33 4
 
0.2%
32 12
0.5%
31 11
0.5%
30 11
0.5%

Customer_For
Real number (ℝ)

Distinct663
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0549497 × 1016
Minimum0
Maximum6.03936 × 1016
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2024-02-16T10:48:41.015344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2832 × 1015
Q11.56168 × 1016
median3.07152 × 1016
Q34.57056 × 1016
95-th percentile5.76288 × 1016
Maximum6.03936 × 1016
Range6.03936 × 1016
Interquartile range (IQR)3.00888 × 1016

Descriptive statistics

Standard deviation1.7463385 × 1016
Coefficient of variation (CV)0.57164231
Kurtosis-1.1946504
Mean3.0549497 × 1016
Median Absolute Deviation (MAD)1.49904 × 1016
Skewness-0.015216086
Sum-5.3561027 × 1018
Variance3.0496982 × 1032
MonotonicityNot monotonic
2024-02-16T10:48:41.171968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.76288 × 101612
 
0.5%
5.6592 × 101611
 
0.5%
4.32 × 101611
 
0.5%
4.1472 × 101511
 
0.5%
2.70432 × 101610
 
0.4%
3.2832 × 101510
 
0.4%
7.344 × 10159
 
0.4%
8.4672 × 10159
 
0.4%
4.69152 × 10169
 
0.4%
1.0368 × 10169
 
0.4%
Other values (653) 2139
95.5%
ValueCountFrequency (%)
0 2
 
0.1%
8.64 × 10133
0.1%
1.728 × 10143
0.1%
2.592 × 10144
0.2%
3.456 × 10145
0.2%
4.32 × 10142
 
0.1%
5.184 × 10142
 
0.1%
6.048 × 10145
0.2%
6.912 × 10142
 
0.1%
7.776 × 10142
 
0.1%
ValueCountFrequency (%)
6.03936 × 10161
 
< 0.1%
6.03072 × 10161
 
< 0.1%
6.02208 × 10164
0.2%
6.01344 × 10163
0.1%
6.0048 × 10165
0.2%
5.99616 × 10164
0.2%
5.98752 × 10161
 
< 0.1%
5.97888 × 10163
0.1%
5.97024 × 10164
0.2%
5.9616 × 10167
0.3%

Interactions

2024-02-16T10:48:26.727029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:49.480285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:51.258664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:53.072365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:55.014302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:58.050542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:00.014305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:02.539028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:04.694104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:06.909526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:08.943486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:11.657037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:13.691753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:15.799038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:18.206160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:20.664629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:24.113432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:26.898500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:49.583611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:51.361591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:53.175528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:55.120097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:58.198922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:00.126639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:02.685122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:04.809223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:07.010527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:09.066186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:11.771232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:13.797603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:15.922533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:18.331903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:20.775135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:24.225367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:27.039697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:49.691631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:51.464880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:53.277882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:55.226890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:58.364715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:00.245949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:02.802755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:04.936634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:07.114018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:09.207728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:11.915371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:13.920322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:16.054716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:18.460526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:20.949605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:24.343092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:27.160527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:49.795070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:51.566822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:53.375241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:55.328494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:58.498680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-02-16T10:47:51.058338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:52.847753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:54.790109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:57.795430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:59.799789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:02.247113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:04.497469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:06.712428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:08.730075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:11.117961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:13.450531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:15.586877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:17.967556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:20.408748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:23.875023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:26.359606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:29.060564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:51.155924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:52.945526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:54.902353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:57.899251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:47:59.907909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:02.363972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:04.590894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:06.806180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:08.830537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:11.519356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:13.563657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:15.695691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:18.081021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:20.537052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:23.976835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T10:48:26.540886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-02-16T10:48:29.427301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-16T10:48:30.201669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Year_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseKidsExpensesTotalAcceptedCmpNumTotalPurchasesCustomer_For
01957UndergraduateSingle58138.0002012-09-045863588546172888838104700000010161702557283200000000000
11954UndergraduateSingle46344.0112014-03-08381116216211250000000227069763200000000000
21965UndergraduateIn a Relationship71613.0002013-08-21264264912711121421821040000000077602126956800000000000
31984UndergraduateIn a Relationship26646.0102014-02-10261142010352204600000001530812009600000000000
41981PostgraduateIn a Relationship58293.0102014-01-199417343118462715553650000000142201913910400000000000
51967PostgraduateIn a Relationship62513.0012013-09-09165204298042142641060000000171602225315200000000000
61971UndergraduateSingle55635.0012012-11-133423565164504927473760000000159002151235200000000000
71985PostgraduateIn a Relationship33454.0102013-05-08327610563123240480000000116901036028800000000000
81974PostgraduateIn a Relationship30351.0102013-06-0619140243321302900000011460633523200000000000
91950PostgraduateIn a Relationship5648.0112014-03-1368280611131100201000000249129331200000000000
Year_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseKidsExpensesTotalAcceptedCmpNumTotalPurchasesCustomer_For
22301984UndergraduateSingle11012.0102013-03-16822432671233312910000001841940608000000000000
22311970PostgraduateSingle44802.0002012-08-21718531014313102029412800000000104902758492800000000000
22321986UndergraduateSingle26816.0002012-08-17505163431003400000000220458838400000000000
22331977UndergraduateIn a Relationship666666.0102013-06-022391418811243136000000016201133868800000000000
22341974UndergraduateIn a Relationship34421.0102013-07-01813376291102700000001300431363200000000000
22351967UndergraduateIn a Relationship61223.0012013-06-134670943182421182472934500000001134101832918400000000000
22361946PostgraduateIn a Relationship64014.0212014-06-105640603000878257000100034441221641600000000000
22371981UndergraduateSingle56981.0002014-01-25919084821732122412313601000000124111913392000000000000
22381956PostgraduateIn a Relationship69245.0012014-01-248428302148030612651030000000184302313478400000000000
22391954PostgraduateIn a Relationship52869.0112012-10-1540843612121331470000001217201153740800000000000

Duplicate rows

Most frequently occurring

Year_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseKidsExpensesTotalAcceptedCmpNumTotalPurchasesCustomer_For# duplicates
171952UndergraduateIn a Relationship83844.0002013-05-1257901313457531191144111001000001574120356832000000000003
381959UndergraduateIn a Relationship18690.0002012-12-28776172341911128000000006005473472000000000003
791968PostgraduateSingle63841.0012013-04-2164635151002071311939600000001908022374976000000000003
1171974UndergraduateIn a Relationship67445.0012012-08-126375780217298011596126000000011174032592704000000000003
1561983UndergraduateIn a Relationship39922.0102013-02-143029125919136230480000000115609432000000000000003
1741990UndergraduateIn a Relationship18929.0002013-02-161532082341811046000000008506430272000000000003
01943PostgraduateSingle48948.0002013-02-01534378206160494227105610000010902124443232000000000002
11946PostgraduateIn a Relationship51012.0002013-04-188610296329241414600000000209010377568000000000002
21946PostgraduateIn a Relationship64014.0212014-06-1056406030008782570001000344412216416000000000002
31946PostgraduateIn a Relationship66835.0002013-09-2821620261953417141164132000000001033024236736000000000002